{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example uses trunk version of numba at Github."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numba import jit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important api of numba is the decorator: `jit`.\n",
    "\n",
    "The `jit` decorator returns a compiled version of the function using the input types and the output types of the function.   You can optionally specify the type using `out_type(in_type, ...)` syntax.  Array inputs can be specified using `[:,:]` appended to the type.   If no type are specified, it watches for what types you call the function with and infers the type of the return.  If there is a previously compiled version of the code available it uses it, if not it generates machine code for the function and then executes that code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sum(arr):\n",
    "    M, N = arr.shape\n",
    "    sum = 0.0\n",
    "    for i in range(M):\n",
    "        for j in range(N):\n",
    "            sum += arr[i,j]\n",
    "    return sum\n",
    "fastsum = jit('f8(f8[:,:])')(sum)\n",
    "flexsum = jit(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "179700.0\n",
      "179700.0\n",
      "179700.0\n",
      "179700.0\n"
     ]
    }
   ],
   "source": [
    "arr2d = np.arange(600,dtype=float).reshape(20,30)\n",
    "print(sum(arr2d))\n",
    "print(fastsum(arr2d))\n",
    "print(flexsum(arr2d))\n",
    "print(flexsum(arr2d.astype(int)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 135 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit sum(arr2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 8.39 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "1000000 loops, best of 3: 827 ns per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit fastsum(arr2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 15.67 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "100000 loops, best of 3: 3.9 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit arr2d.sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The speed-up is even more pronounced the more inner loops in the code.   Here is an image processing example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@jit('(f8[:,:],f8[:,:],f8[:,:])')\n",
    "def filter(image, filt, output):\n",
    "    M, N = image.shape\n",
    "    m, n = filt.shape\n",
    "    for i in range(m//2, M-m//2):\n",
    "        for j in range(n//2, N-n//2):\n",
    "            result = 0.0\n",
    "            for k in range(m):\n",
    "                for l in range(n):\n",
    "                    result += image[i+k-m//2,j+l-n//2]*filt[k, l]\n",
    "            output[i,j] = result\n",
    "\n",
    "try:\n",
    "    # py2\n",
    "    from urllib import urlopen\n",
    "except ImportError:\n",
    "    # py3\n",
    "    from urllib.request import urlopen\n",
    "\n",
    "bytes = urlopen('http://www.cs.tut.fi/~foi/SA-DCT/original/image_Lake512.png').read()            \n",
    "\n",
    "from matplotlib.pyplot import imread\n",
    "\n",
    "from io import BytesIO\n",
    "\n",
    "image = imread(BytesIO(bytes)).astype('double')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C+fl5RkdH6evrU2HUvY77qiSsVivvvfce8/PzDA0NYbPZ6Ovro6uri3K5zPT0tMIIbDYb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3V1NYqKivD888/D4/GgpqYGN2/elIiHUeKtHLfVSezZswc3b94EsHbTbDYbiouLMTU1BZPJhPLy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\njo8c/x890C13QIRTqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df81eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(image)\n",
    "gray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 1.250645 s; Numba: 2.591848 ms; Speed up is 482.530126\n"
     ]
    },
    {
     "data": {
      "image/png": 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WOobp6FmU8f0V7meA4juQuqng4Xqud99FLi8o15+SkuLBNPIQsEdVMJNarZaG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\n6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NHR0ez5jA9cvXoVH/3oR1GtVvHGG2/g9OnTmJqawh133IFDhw5heHgYe/bswde+9rXM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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10edb3c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "filt = np.ones((15,15),dtype='double')\n",
    "filt /= filt.sum()\n",
    "output = image.copy()\n",
    "filter(image, filt, output)\n",
    "gray()\n",
    "imshow(output)\n",
    "start = time.time()\n",
    "filter(image[:100,:100], filt, output[:100,:100])\n",
    "fast = time.time() - start\n",
    "start = time.time()\n",
    "filter.py_func(image[:100,:100], filt, output[:100,:100])\n",
    "slow = time.time() - start\n",
    "print(\"Python: %f s; Numba: %f ms; Speed up is %f\" % (slow, fast*1000, slow / fast))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can call Numba-created functions from other Numba-created functions and get even more amazing speed-ups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nyEplIel4A6XRec57sp7TOJffcZXZrhJO5RT6JFCYOCRolCK/0womczU9aECG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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e88e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@jit\n",
    "def mandel(x, y, max_iters):\n",
    "    \"\"\"\n",
    "    Given the real and imaginary parts of a complex number,\n",
    "    determine if it is a candidate for membership in the Mandelbrot\n",
    "    set given a fixed number of iterations.\n",
    "    \"\"\"\n",
    "    i = 0\n",
    "    c = complex(x, y)\n",
    "    z = 0.0j\n",
    "    for i in range(max_iters):\n",
    "        z = z*z + c\n",
    "        if (z.real*z.real + z.imag*z.imag) >= 4:\n",
    "            return i\n",
    "\n",
    "    return 255\n",
    "\n",
    "@jit\n",
    "def create_fractal(min_x, max_x, min_y, max_y, image, iters):\n",
    "    height = image.shape[0]\n",
    "    width = image.shape[1]\n",
    "\n",
    "    pixel_size_x = (max_x - min_x) / width\n",
    "    pixel_size_y = (max_y - min_y) / height\n",
    "    for x in range(width):\n",
    "        real = min_x + x * pixel_size_x\n",
    "        for y in range(height):\n",
    "            imag = min_y + y * pixel_size_y\n",
    "            color = mandel(real, imag, iters)\n",
    "            image[y, x] = color\n",
    "\n",
    "    return image\n",
    "\n",
    "image = np.zeros((500, 750), dtype=np.uint8)\n",
    "imshow(create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20))\n",
    "jet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 12.5 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 175 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit create_fractal.py_func(-2.0, 1.0, -1.0, 1.0, image, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic complex support is available as well.  Some functions are still being implemented, however."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((-12-16j), (-12-16j))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@jit\n",
    "def complex_support(real, imag):\n",
    "    c = complex(real, imag)\n",
    "    return (c ** 2).conjugate()\n",
    "\n",
    "c = 2.0 + 4.0j\n",
    "complex_support(c.real, c.imag), (c**2).conjugate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even create a function that takes a structured array as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.23606798  5.        ]\n",
      "[(array(Record([('x', '<f8'), ('y', '<f8')]), 1d, C),)]\n",
      "; ModuleID = 'hypot'\n",
      "target datalayout = \"e-m:o-i64:64-f80:128-n8:16:32:64-S128\"\n",
      "target triple = \"x86_64-apple-darwin15.3.0\"\n",
      "\n",
      "@.const.hypot = internal constant [6 x i8] c\"hypot\\00\"\n",
      "@\".const.Fatal error: missing _dynfunc.Closure\" = internal constant [38 x i8] c\"Fatal error: missing _dynfunc.Closure\\00\"\n",
      "@PyExc_RuntimeError = external global i8\n",
      "@\".const.missing Environment\" = internal constant [20 x i8] c\"missing Environment\\00\"\n",
      "\n",
      "define i32 @\"__main__.hypot$8.array(Record_515,_1d,_C)\"({ i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* noalias nocapture %retptr, { i8*, i32 }** noalias nocapture readnone %excinfo, i8* noalias nocapture readnone %env, i8* %arg.data.0, i8* nocapture readnone %arg.data.1, i64 %arg.data.2, i64 %arg.data.3, [16 x i8]* nocapture readonly %arg.data.4, i64 %arg.data.5.0, i64 %arg.data.6.0) {\n",
      "entry:\n",
      "  %.4.i = icmp eq i8* %arg.data.0, null\n",
      "  br i1 %.4.i, label %NRT_incref.exit, label %.3.endif.i, !prof !0\n",
      "\n",
      ".3.endif.i:                                       ; preds = %entry\n",
      "  %.7.i = bitcast i8* %arg.data.0 to i64*\n",
      "  %.4.i.i = atomicrmw add i64* %.7.i, i64 1 monotonic\n",
      "  br label %NRT_incref.exit\n",
      "\n",
      "NRT_incref.exit:                                  ; preds = %entry, %.3.endif.i\n",
      "  %.50 = shl i64 %arg.data.5.0, 3\n",
      "  %.51 = tail call i8* @NRT_MemInfo_alloc_safe_aligned(i64 %.50, i32 32)\n",
      "  %.5.i = getelementptr i8, i8* %.51, i64 24\n",
      "  %0 = bitcast i8* %.5.i to i8**\n",
      "  %.6.i = load i8*, i8** %0, align 8\n",
      "  %.161 = icmp sgt i64 %arg.data.5.0, 0\n",
      "  br i1 %.161, label %B47.preheader, label %B112\n",
      "\n",
      "B47.preheader:                                    ; preds = %NRT_incref.exit\n",
      "  %1 = add i64 %arg.data.5.0, 1\n",
      "  %scevgep31 = getelementptr [16 x i8], [16 x i8]* %arg.data.4, i64 0, i64 8\n",
      "  %scevgep3132 = bitcast i8* %scevgep31 to [16 x i8]*\n",
      "  br label %B47\n",
      "\n",
      "B47:                                              ; preds = %B47.preheader, %B47\n",
      "  %lsr.iv33 = phi [16 x i8]* [ %scevgep3132, %B47.preheader ], [ %2, %B47 ]\n",
      "  %lsr.iv29 = phi i8* [ %.6.i, %B47.preheader ], [ %scevgep, %B47 ]\n",
      "  %lsr.iv = phi i64 [ %1, %B47.preheader ], [ %lsr.iv.next, %B47 ]\n",
      "  %lsr.iv3335 = bitcast [16 x i8]* %lsr.iv33 to double*\n",
      "  %lsr.iv2930 = bitcast i8* %lsr.iv29 to double*\n",
      "  %scevgep36 = getelementptr double, double* %lsr.iv3335, i64 -1\n",
      "  %.306 = load double, double* %scevgep36, align 8\n",
      "  %.398 = load double, double* %lsr.iv3335, align 8\n",
      "  %.447 = fmul double %.398, %.398\n",
      "  %.355 = fmul double %.306, %.306\n",
      "  %.457 = fadd double %.355, %.447\n",
      "  %.471.le = tail call double @sqrt(double %.457)\n",
      "  store double %.471.le, double* %lsr.iv2930, align 8\n",
      "  %lsr.iv.next = add i64 %lsr.iv, -1\n",
      "  %scevgep = getelementptr i8, i8* %lsr.iv29, i64 8\n",
      "  %scevgep34 = getelementptr [16 x i8], [16 x i8]* %lsr.iv33, i64 1, i64 0\n",
      "  %2 = bitcast i8* %scevgep34 to [16 x i8]*\n",
      "  %.204 = icmp sgt i64 %lsr.iv.next, 1\n",
      "  br i1 %.204, label %B47, label %B112\n",
      "\n",
      "B112:                                             ; preds = %B47, %NRT_incref.exit\n",
      "  %3 = icmp eq i8* %arg.data.0, null\n",
      "  br i1 %3, label %NRT_decref.exit, label %.3.endif.i18, !prof !0\n",
      "\n",
      ".3.endif.i18:                                     ; preds = %B112\n",
      "  %.7.i16 = bitcast i8* %arg.data.0 to i64*\n",
      "  %.4.i.i17 = atomicrmw sub i64* %.7.i16, i64 1 monotonic\n",
      "  %.9.i = icmp eq i64 %.4.i.i17, 1\n",
      "  br i1 %.9.i, label %.3.endif.if.i, label %NRT_decref.exit, !prof !0\n",
      "\n",
      ".3.endif.if.i:                                    ; preds = %.3.endif.i18\n",
      "  tail call void @NRT_MemInfo_call_dtor(i8* nonnull %arg.data.0)\n",
      "  br label %NRT_decref.exit\n",
      "\n",
      "NRT_decref.exit:                                  ; preds = %B112, %.3.endif.i18, %.3.endif.if.i\n",
      "  %retptr.repack37 = bitcast { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %retptr to i8**\n",
      "  store i8* %.51, i8** %retptr.repack37, align 8\n",
      "  %retptr.repack1 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %retptr, i64 0, i32 1\n",
      "  store i8* null, i8** %retptr.repack1, align 8\n",
      "  %retptr.repack3 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %retptr, i64 0, i32 2\n",
      "  store i64 %arg.data.5.0, i64* %retptr.repack3, align 8\n",
      "  %retptr.repack5 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %retptr, i64 0, i32 3\n",
      "  store i64 8, i64* %retptr.repack5, align 8\n",
      "  %retptr.repack7 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %retptr, i64 0, i32 4\n",
      "  %4 = bitcast double** %retptr.repack7 to i8**\n",
      "  store i8* %.6.i, i8** %4, align 8\n",
      "  %5 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %retptr, i64 0, i32 5, i64 0\n",
      "  store i64 %arg.data.5.0, i64* %5, align 8\n",
      "  %6 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %retptr, i64 0, i32 6, i64 0\n",
      "  store i64 8, i64* %6, align 8\n",
      "  ret i32 0\n",
      "}\n",
      "\n",
      "declare noalias i8* @NRT_MemInfo_alloc_safe_aligned(i64, i32)\n",
      "\n",
      "; Function Attrs: nounwind readonly\n",
      "declare double @sqrt(double) #0\n",
      "\n",
      "define i8* @\"cpython.__main__.hypot$8.array(Record_515,_1d,_C)\"(i8* %py_closure, i8* %py_args, i8* nocapture readnone %py_kws) {\n",
      "entry:\n",
      "  %.5 = alloca i8*, align 8\n",
      "  %.6 = call i32 (i8*, i8*, i64, i64, ...) @PyArg_UnpackTuple(i8* %py_args, i8* getelementptr inbounds ([6 x i8], [6 x i8]* @.const.hypot, i64 0, i64 0), i64 1, i64 1, i8** nonnull %.5)\n",
      "  %.7 = icmp eq i32 %.6, 0\n",
      "  %.30 = alloca { i8*, i8*, i64, i64, [16 x i8]*, [1 x i64], [1 x i64] }, align 8\n",
      "  %.77 = alloca { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, align 8\n",
      "  %0 = bitcast { i8*, i8*, i64, i64, [16 x i8]*, [1 x i64], [1 x i64] }* %.30 to i8*\n",
      "  call void @llvm.memset.p0i8.i64(i8* %0, i8 0, i64 56, i32 8, i1 false)\n",
      "  br i1 %.7, label %entry.if, label %entry.endif, !prof !0\n",
      "\n",
      "entry.if:                                         ; preds = %entry.endif.endif.endif, %entry\n",
      "  ret i8* null\n",
      "\n",
      "entry.endif:                                      ; preds = %entry\n",
      "  %.11 = icmp eq i8* %py_closure, null\n",
      "  br i1 %.11, label %entry.endif.if, label %entry.endif.endif, !prof !0\n",
      "\n",
      "entry.endif.if:                                   ; preds = %entry.endif\n",
      "  %.13 = call i32 @puts(i8* nonnull getelementptr inbounds ([38 x i8], [38 x i8]* @\".const.Fatal error: missing _dynfunc.Closure\", i64 0, i64 0))\n",
      "  unreachable\n",
      "\n",
      "entry.endif.endif:                                ; preds = %entry.endif\n",
      "  %.15 = ptrtoint i8* %py_closure to i64\n",
      "  %.16 = add i64 %.15, 24\n",
      "  %.18 = inttoptr i64 %.16 to { i8* }*\n",
      "  %.1910 = bitcast { i8* }* %.18 to i8**\n",
      "  %.20 = load i8*, i8** %.1910, align 8\n",
      "  %.25 = icmp eq i8* %.20, null\n",
      "  br i1 %.25, label %entry.endif.endif.if, label %entry.endif.endif.endif, !prof !0\n",
      "\n",
      "entry.endif.endif.if:                             ; preds = %entry.endif.endif\n",
      "  call void @PyErr_SetString(i8* nonnull @PyExc_RuntimeError, i8* nonnull getelementptr inbounds ([20 x i8], [20 x i8]* @\".const.missing Environment\", i64 0, i64 0))\n",
      "  ret i8* null\n",
      "\n",
      "entry.endif.endif.endif:                          ; preds = %entry.endif.endif\n",
      "  %1 = bitcast { i8*, i8*, i64, i64, [16 x i8]*, [1 x i64], [1 x i64] }* %.30 to i8**\n",
      "  %.29 = load i8*, i8** %.5, align 8\n",
      "  %.32 = bitcast { i8*, i8*, i64, i64, [16 x i8]*, [1 x i64], [1 x i64] }* %.30 to i8*\n",
      "  %.33 = call i32 @NRT_adapt_ndarray_from_python(i8* %.29, i8* %.32)\n",
      "  %.34 = icmp eq i32 %.33, 0\n",
      "  %2 = load i8*, i8** %1, align 8\n",
      "  br i1 %.34, label %entry.endif.endif.endif.endif, label %entry.if, !prof !1\n",
      "\n",
      "entry.endif.endif.endif.endif:                    ; preds = %entry.endif.endif.endif\n",
      "  %sunkaddr = ptrtoint { i8*, i8*, i64, i64, [16 x i8]*, [1 x i64], [1 x i64] }* %.30 to i64\n",
      "  %sunkaddr11 = add i64 %sunkaddr, 32\n",
      "  %sunkaddr12 = inttoptr i64 %sunkaddr11 to [16 x i8]**\n",
      "  %3 = load [16 x i8]*, [16 x i8]** %sunkaddr12, align 8\n",
      "  %sunkaddr13 = ptrtoint { i8*, i8*, i64, i64, [16 x i8]*, [1 x i64], [1 x i64] }* %.30 to i64\n",
      "  %sunkaddr14 = add i64 %sunkaddr13, 40\n",
      "  %sunkaddr15 = inttoptr i64 %sunkaddr14 to i64*\n",
      "  %4 = load i64, i64* %sunkaddr15, align 8\n",
      "  %.4.i.i = icmp eq i8* %2, null\n",
      "  br i1 %.4.i.i, label %NRT_incref.exit.i, label %.3.endif.i.i, !prof !0\n",
      "\n",
      ".3.endif.i.i:                                     ; preds = %entry.endif.endif.endif.endif\n",
      "  %.7.i.i = bitcast i8* %2 to i64*\n",
      "  %.4.i.i.i = atomicrmw add i64* %.7.i.i, i64 1 monotonic, !noalias !2\n",
      "  br label %NRT_incref.exit.i\n",
      "\n",
      "NRT_incref.exit.i:                                ; preds = %.3.endif.i.i, %entry.endif.endif.endif.endif\n",
      "  %.50.i = shl i64 %4, 3\n",
      "  %.51.i = call i8* @NRT_MemInfo_alloc_safe_aligned(i64 %.50.i, i32 32), !noalias !2\n",
      "  %.5.i.i = getelementptr i8, i8* %.51.i, i64 24\n",
      "  %5 = bitcast i8* %.5.i.i to i8**\n",
      "  %.6.i.i = load i8*, i8** %5, align 8, !noalias !2\n",
      "  %.161.i = icmp sgt i64 %4, 0\n",
      "  br i1 %.161.i, label %B47.i.preheader, label %B112.i\n",
      "\n",
      "B47.i.preheader:                                  ; preds = %NRT_incref.exit.i\n",
      "  %6 = add i64 %4, 1\n",
      "  %scevgep4 = getelementptr [16 x i8], [16 x i8]* %3, i64 0, i64 8\n",
      "  %scevgep45 = bitcast i8* %scevgep4 to [16 x i8]*\n",
      "  br label %B47.i\n",
      "\n",
      "B47.i:                                            ; preds = %B47.i.preheader, %B47.i\n",
      "  %lsr.iv6 = phi [16 x i8]* [ %scevgep45, %B47.i.preheader ], [ %7, %B47.i ]\n",
      "  %lsr.iv2 = phi i8* [ %.6.i.i, %B47.i.preheader ], [ %scevgep, %B47.i ]\n",
      "  %lsr.iv = phi i64 [ %6, %B47.i.preheader ], [ %lsr.iv.next, %B47.i ]\n",
      "  %lsr.iv68 = bitcast [16 x i8]* %lsr.iv6 to double*\n",
      "  %lsr.iv23 = bitcast i8* %lsr.iv2 to double*\n",
      "  %scevgep9 = getelementptr double, double* %lsr.iv68, i64 -1\n",
      "  %.306.i = load double, double* %scevgep9, align 8, !noalias !2\n",
      "  %.398.i = load double, double* %lsr.iv68, align 8, !noalias !2\n",
      "  %.447.i = fmul double %.398.i, %.398.i\n",
      "  %.355.i = fmul double %.306.i, %.306.i\n",
      "  %.457.i = fadd double %.355.i, %.447.i\n",
      "  %.471.le.i = call double @sqrt(double %.457.i), !noalias !2\n",
      "  store double %.471.le.i, double* %lsr.iv23, align 8, !noalias !2\n",
      "  %lsr.iv.next = add i64 %lsr.iv, -1\n",
      "  %scevgep = getelementptr i8, i8* %lsr.iv2, i64 8\n",
      "  %scevgep7 = getelementptr [16 x i8], [16 x i8]* %lsr.iv6, i64 1, i64 0\n",
      "  %7 = bitcast i8* %scevgep7 to [16 x i8]*\n",
      "  %.204.i = icmp sgt i64 %lsr.iv.next, 1\n",
      "  br i1 %.204.i, label %B47.i, label %B112.i\n",
      "\n",
      "B112.i:                                           ; preds = %B47.i, %NRT_incref.exit.i\n",
      "  %8 = icmp eq i8* %2, null\n",
      "  br i1 %8, label %\"__main__.hypot$8.array(Record_515,_1d,_C).exit\", label %.3.endif.i18.i, !prof !0\n",
      "\n",
      ".3.endif.i18.i:                                   ; preds = %B112.i\n",
      "  %.7.i16.i = bitcast i8* %2 to i64*\n",
      "  %.4.i.i17.i = atomicrmw sub i64* %.7.i16.i, i64 1 monotonic, !noalias !2\n",
      "  %.9.i.i = icmp eq i64 %.4.i.i17.i, 1\n",
      "  br i1 %.9.i.i, label %.3.endif.if.i.i, label %.3.endif.i, !prof !0\n",
      "\n",
      ".3.endif.if.i.i:                                  ; preds = %.3.endif.i18.i\n",
      "  call void @NRT_MemInfo_call_dtor(i8* nonnull %2), !noalias !2\n",
      "  br label %.3.endif.i\n",
      "\n",
      "\"__main__.hypot$8.array(Record_515,_1d,_C).exit\": ; preds = %B112.i\n",
      "  %9 = ptrtoint i8* %.51.i to i64\n",
      "  %10 = ptrtoint i8* %.6.i.i to i64\n",
      "  br label %NRT_decref.exit\n",
      "\n",
      ".3.endif.i:                                       ; preds = %.3.endif.i18.i, %.3.endif.if.i.i\n",
      "  %11 = bitcast i8* %2 to i64*\n",
      "  %12 = ptrtoint i8* %.51.i to i64\n",
      "  %13 = ptrtoint i8* %.6.i.i to i64\n",
      "  %.4.i.i1 = atomicrmw sub i64* %11, i64 1 monotonic\n",
      "  %.9.i = icmp eq i64 %.4.i.i1, 1\n",
      "  br i1 %.9.i, label %.3.endif.if.i, label %NRT_decref.exit, !prof !0\n",
      "\n",
      ".3.endif.if.i:                                    ; preds = %.3.endif.i\n",
      "  %14 = ptrtoint i8* %.6.i.i to i64\n",
      "  %15 = ptrtoint i8* %.51.i to i64\n",
      "  call void @NRT_MemInfo_call_dtor(i8* nonnull %2)\n",
      "  br label %NRT_decref.exit\n",
      "\n",
      "NRT_decref.exit:                                  ; preds = %.3.endif.i, %\"__main__.hypot$8.array(Record_515,_1d,_C).exit\", %.3.endif.if.i\n",
      "  %16 = phi i64 [ %14, %.3.endif.if.i ], [ %13, %.3.endif.i ], [ %10, %\"__main__.hypot$8.array(Record_515,_1d,_C).exit\" ]\n",
      "  %17 = phi i64 [ %15, %.3.endif.if.i ], [ %12, %.3.endif.i ], [ %9, %\"__main__.hypot$8.array(Record_515,_1d,_C).exit\" ]\n",
      "  %sunkaddr16 = ptrtoint i8* %.20 to i64\n",
      "  %sunkaddr17 = add i64 %sunkaddr16, 24\n",
      "  %sunkaddr18 = inttoptr i64 %sunkaddr17 to i8**\n",
      "  %.75 = load i8*, i8** %sunkaddr18, align 8\n",
      "  %.76 = call i8* @PyList_GetItem(i8* %.75, i64 0)\n",
      "  %18 = bitcast { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %.77 to i64*\n",
      "  store i64 %17, i64* %18, align 8\n",
      "  %19 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %.77, i64 0, i32 1\n",
      "  %20 = bitcast i8** %19 to i64*\n",
      "  store i64 0, i64* %20, align 8\n",
      "  %21 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %.77, i64 0, i32 2\n",
      "  store i64 %4, i64* %21, align 8\n",
      "  %22 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %.77, i64 0, i32 3\n",
      "  store i64 8, i64* %22, align 8\n",
      "  %23 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %.77, i64 0, i32 4\n",
      "  %24 = bitcast double** %23 to i64*\n",
      "  store i64 %16, i64* %24, align 8\n",
      "  %25 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %.77, i64 0, i32 5, i64 0\n",
      "  store i64 %4, i64* %25, align 8\n",
      "  %26 = getelementptr inbounds { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }, { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %.77, i64 0, i32 6, i64 0\n",
      "  store i64 8, i64* %26, align 8\n",
      "  %.79 = bitcast { i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64] }* %.77 to i8*\n",
      "  %.80 = call i8* @NRT_adapt_ndarray_to_python(i8* %.79, i32 1, i32 1, i8* %.76)\n",
      "  ret i8* %.80\n",
      "}\n",
      "\n",
      "declare i32 @PyArg_UnpackTuple(i8*, i8*, i64, i64, ...)\n",
      "\n",
      "; Function Attrs: nounwind\n",
      "declare i32 @puts(i8* nocapture readonly) #1\n",
      "\n",
      "declare void @PyErr_SetString(i8*, i8*)\n",
      "\n",
      "declare i32 @NRT_adapt_ndarray_from_python(i8* nocapture, i8* nocapture)\n",
      "\n",
      "declare i8* @PyList_GetItem(i8*, i64)\n",
      "\n",
      "declare i8* @NRT_adapt_ndarray_to_python(i8* nocapture, i32, i32, i8*)\n",
      "\n",
      "declare void @NRT_MemInfo_call_dtor(i8*)\n",
      "\n",
      "; Function Attrs: argmemonly nounwind\n",
      "declare void @llvm.memset.p0i8.i64(i8* nocapture, i8, i64, i32, i1) #2\n",
      "\n",
      "attributes #0 = { nounwind readonly }\n",
      "attributes #1 = { nounwind }\n",
      "attributes #2 = { argmemonly nounwind }\n",
      "\n",
      "!0 = !{!\"branch_weights\", i32 1, i32 99}\n",
      "!1 = !{!\"branch_weights\", i32 99, i32 1}\n",
      "!2 = !{!3}\n",
      "!3 = distinct !{!3, !4, !\"__main__.hypot$8.array(Record_515,_1d,_C): %retptr\"}\n",
      "!4 = distinct !{!4, !\"__main__.hypot$8.array(Record_515,_1d,_C)\"}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from numba import jit, typeof\n",
    "import numpy as np\n",
    "\n",
    "record_dtype = np.dtype([('x', np.float64), ('y', np.float64)], align=True)\n",
    "record_type = typeof(record_dtype)\n",
    "a = np.array([(1.0, 2.0), (3.0, 4.0)], dtype=record_dtype)\n",
    "\n",
    "@jit\n",
    "def hypot(data):\n",
    "    # return types of numpy functions are inferred\n",
    "    result = np.empty_like(data, dtype=np.float64)\n",
    "    # notice access to structure elements 'x' and 'y' via attribute access\n",
    "    # You can also index by field name or field index:\n",
    "    #       data[i].x == data[i]['x'] == data[i][0]\n",
    "    for i in range(data.shape[0]):\n",
    "        result[i] = np.sqrt(data[i].x * data[i].x + data[i].y * data[i].y)\n",
    "\n",
    "    return result\n",
    "\n",
    "print(hypot(a))\n",
    "\n",
    "# Notice inferred return type\n",
    "print(hypot.signatures)\n",
    "# Notice native sqrt calls and for.body direct access to memory...\n",
    "llvmir = hypot.inspect_llvm(hypot.signatures[0])\n",
    "print(llvmir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(array(Record([('x', '<f8'), ('y', '<f8')]), 1d, C),)]\n"
     ]
    }
   ],
   "source": [
    "print(hypot.signatures) # inspect function signature, note inferred return type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['  %.471.le = tail call double @sqrt(double %.457)',\n",
       " 'declare double @sqrt(double) #0',\n",
       " '  %.471.le.i = call double @sqrt(double %.457.i), !noalias !2']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[line for line in llvmir.splitlines() if 'sqrt' in line] # note native math calls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have questions?  Join the mailing list at numba-users@continuum.io"
   ]
  }
 ],
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